| 引用本文: | 张智军,孙健声,陈博钊.动态收敛微分神经网络帕金森病诊断系统[J].控制理论与应用,2021,38(8):1213~1218.[点击复制] |
| ZHANG Zhi-jun,SUN Jian-sheng,CHEN Bo-zhao.Dynamic convergent differential neural network for Parkinson’s disease diagnosis system[J].Control Theory and Technology,2021,38(8):1213~1218.[点击复制] |
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| 动态收敛微分神经网络帕金森病诊断系统 |
| Dynamic convergent differential neural network for Parkinson’s disease diagnosis system |
| 摘要点击 1674 全文点击 497 投稿时间：2020-11-03 修订日期：2021-06-12 |
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| DOI编号 10.7641/CTA.2021.00770 |
| 2021,38(8):1213-1218 |
| 中文关键词 神经动力学 神经网络 机器学习 帕金森病 |
| 英文关键词 neural dynamics neural networks machine learning Parkinson’s disease |
| 基金项目 国家自然科学基金项目(61976096), 广东省基础与应用基础研究基金项目(2020B1515120047), 广东省杰出青年基金项目(2017A030306009), 广东 特支计划科技创新青年拔尖人才项目(2017TQ04X475), 华南理工大学–天下谷联合实验室基金项目(x2zdD8212590), 华南理工大学科研启动基 金项目, 国家重点研发计划(2017YFB1002505), 广东省重点研发基金(2018B030339001), 广东省自然科学基金团队项目(1414060000024)资助. |
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| 中文摘要 |
| 帕金森病是一种在老年人中常见的神经系统疾病, 它影响患者的说话、写字和平衡能力. 根据声学特征对
帕金森病进行诊断可以尽早发现病情, 有利于早日介入治疗. 基于神经动力学方法, 本文提出了一种动态收敛微分
神经网络(dynamic convergent differential neural network, DCDNN), 利用帕金森病的声学特征进行识别诊断. 此前,
对样本进行标准化、统计池特征增强和主成分分析. 数值仿真实验表明所提出方法获得97.22%的准确率, 比现存
最好方法高斯支持向量机(Gaussian SVM)高约6个百分点. |
| 英文摘要 |
| Parkinson’s disease is a common nervous system disease existing in the elderly. It affects a patient’s ability to
speak, write and balance. The diagnosis of Parkinson’s disease based on its acoustic features can help to find the disease as
early as possible, which is conducive to early intervention treatment. In this paper, based on the neural dynamics method,
a novel dynamic convergent differential neural network (DCDNN) is proposed. It uses the acoustic features of Parkinson’s
disease for recognition and diagnosis. Specifically, the samples of the Parkinson’s disease data set are standardized and the
features are enhanced by the statistical pools. Furthermore, the principal component analysis is applied for feature extraction
and the proposed DCDNN is applied for classification. The computer simulation results verify that the proposed framework
has achieved the classification accuracy of 97.22% on the Parkinson’s disease data set, which is about 6 percentage points
higher than the Gaussian SVM. |
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